Overview

Dataset statistics

Number of variables14
Number of observations506
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.5 KiB
Average record size in memory112.3 B

Variable types

Numeric13
Categorical1

Alerts

CRIM is highly overall correlated with ZN and 8 other fieldsHigh correlation
ZN is highly overall correlated with CRIM and 4 other fieldsHigh correlation
INDUS is highly overall correlated with CRIM and 7 other fieldsHigh correlation
NOX is highly overall correlated with CRIM and 8 other fieldsHigh correlation
RM is highly overall correlated with LSTAT and 1 other fieldsHigh correlation
AGE is highly overall correlated with CRIM and 7 other fieldsHigh correlation
DIS is highly overall correlated with CRIM and 6 other fieldsHigh correlation
RAD is highly overall correlated with CRIM and 2 other fieldsHigh correlation
TAX is highly overall correlated with CRIM and 7 other fieldsHigh correlation
PTRATIO is highly overall correlated with MEDVHigh correlation
LSTAT is highly overall correlated with CRIM and 7 other fieldsHigh correlation
MEDV is highly overall correlated with CRIM and 7 other fieldsHigh correlation
CHAS is highly imbalanced (63.7%)Imbalance
ZN has 372 (73.5%) zerosZeros

Reproduction

Analysis started2023-03-24 06:50:45.120458
Analysis finished2023-03-24 06:51:08.056806
Duration22.94 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

CRIM
Real number (ℝ)

Distinct504
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6135236
Minimum0.00632
Maximum88.9762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:08.175467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.02791
Q10.082045
median0.25651
Q33.6770825
95-th percentile15.78915
Maximum88.9762
Range88.96988
Interquartile range (IQR)3.5950375

Descriptive statistics

Standard deviation8.6015451
Coefficient of variation (CV)2.3803761
Kurtosis37.130509
Mean3.6135236
Median Absolute Deviation (MAD)0.22145
Skewness5.2231488
Sum1828.4429
Variance73.986578
MonotonicityNot monotonic
2023-03-24T15:51:08.340087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01501 2
 
0.4%
14.3337 2
 
0.4%
0.03466 1
 
0.2%
0.03113 1
 
0.2%
0.03049 1
 
0.2%
0.02543 1
 
0.2%
0.02498 1
 
0.2%
0.01301 1
 
0.2%
0.06151 1
 
0.2%
0.05497 1
 
0.2%
Other values (494) 494
97.6%
ValueCountFrequency (%)
0.00632 1
0.2%
0.00906 1
0.2%
0.01096 1
0.2%
0.01301 1
0.2%
0.01311 1
0.2%
0.0136 1
0.2%
0.01381 1
0.2%
0.01432 1
0.2%
0.01439 1
0.2%
0.01501 2
0.4%
ValueCountFrequency (%)
88.9762 1
0.2%
73.5341 1
0.2%
67.9208 1
0.2%
51.1358 1
0.2%
45.7461 1
0.2%
41.5292 1
0.2%
38.3518 1
0.2%
37.6619 1
0.2%
28.6558 1
0.2%
25.9406 1
0.2%

ZN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.363636
Minimum0
Maximum100
Zeros372
Zeros (%)73.5%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:08.485885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.322453
Coefficient of variation (CV)2.0523759
Kurtosis4.0315101
Mean11.363636
Median Absolute Deviation (MAD)0
Skewness2.2256663
Sum5750
Variance543.93681
MonotonicityNot monotonic
2023-03-24T15:51:08.618480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 372
73.5%
20 21
 
4.2%
80 15
 
3.0%
22 10
 
2.0%
12.5 10
 
2.0%
25 10
 
2.0%
40 7
 
1.4%
45 6
 
1.2%
30 6
 
1.2%
90 5
 
1.0%
Other values (16) 44
 
8.7%
ValueCountFrequency (%)
0 372
73.5%
12.5 10
 
2.0%
17.5 1
 
0.2%
18 1
 
0.2%
20 21
 
4.2%
21 4
 
0.8%
22 10
 
2.0%
25 10
 
2.0%
28 3
 
0.6%
30 6
 
1.2%
ValueCountFrequency (%)
100 1
 
0.2%
95 4
 
0.8%
90 5
 
1.0%
85 2
 
0.4%
82.5 2
 
0.4%
80 15
3.0%
75 3
 
0.6%
70 3
 
0.6%
60 4
 
0.8%
55 3
 
0.6%

INDUS
Real number (ℝ)

Distinct76
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.136779
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:08.775062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.89
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.8603529
Coefficient of variation (CV)0.61600874
Kurtosis-1.2335396
Mean11.136779
Median Absolute Deviation (MAD)6.32
Skewness0.29502157
Sum5635.21
Variance47.064442
MonotonicityNot monotonic
2023-03-24T15:51:08.920706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1 132
26.1%
19.58 30
 
5.9%
8.14 22
 
4.3%
6.2 18
 
3.6%
21.89 15
 
3.0%
3.97 12
 
2.4%
9.9 12
 
2.4%
8.56 11
 
2.2%
10.59 11
 
2.2%
5.86 10
 
2.0%
Other values (66) 233
46.0%
ValueCountFrequency (%)
0.46 1
 
0.2%
0.74 1
 
0.2%
1.21 1
 
0.2%
1.22 1
 
0.2%
1.25 2
0.4%
1.32 1
 
0.2%
1.38 1
 
0.2%
1.47 2
0.4%
1.52 4
0.8%
1.69 2
0.4%
ValueCountFrequency (%)
27.74 5
 
1.0%
25.65 7
 
1.4%
21.89 15
 
3.0%
19.58 30
 
5.9%
18.1 132
26.1%
15.04 3
 
0.6%
13.92 5
 
1.0%
13.89 4
 
0.8%
12.83 6
 
1.2%
11.93 5
 
1.0%

CHAS
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
0.0
471 
1.0
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1518
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 471
93.1%
1.0 35
 
6.9%

Length

2023-03-24T15:51:09.070303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T15:51:09.216906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 471
93.1%
1.0 35
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 977
64.4%
. 506
33.3%
1 35
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1012
66.7%
Other Punctuation 506
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 977
96.5%
1 35
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1518
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 977
64.4%
. 506
33.3%
1 35
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 977
64.4%
. 506
33.3%
1 35
 
2.3%

NOX
Real number (ℝ)

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55469506
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:09.338580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.40925
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.11587768
Coefficient of variation (CV)0.20890339
Kurtosis-0.064667133
Mean0.55469506
Median Absolute Deviation (MAD)0.0875
Skewness0.72930792
Sum280.6757
Variance0.013427636
MonotonicityNot monotonic
2023-03-24T15:51:09.503689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.538 23
 
4.5%
0.713 18
 
3.6%
0.437 17
 
3.4%
0.871 16
 
3.2%
0.624 15
 
3.0%
0.489 15
 
3.0%
0.693 14
 
2.8%
0.605 14
 
2.8%
0.74 13
 
2.6%
0.544 12
 
2.4%
Other values (71) 349
69.0%
ValueCountFrequency (%)
0.385 1
 
0.2%
0.389 1
 
0.2%
0.392 2
0.4%
0.394 1
 
0.2%
0.398 2
0.4%
0.4 4
0.8%
0.401 3
0.6%
0.403 3
0.6%
0.404 3
0.6%
0.405 3
0.6%
ValueCountFrequency (%)
0.871 16
3.2%
0.77 8
1.6%
0.74 13
2.6%
0.718 6
 
1.2%
0.713 18
3.6%
0.7 11
2.2%
0.693 14
2.8%
0.679 8
1.6%
0.671 7
 
1.4%
0.668 3
 
0.6%

RM
Real number (ℝ)

Distinct446
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2846344
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:09.682720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.314
Q15.8855
median6.2085
Q36.6235
95-th percentile7.5875
Maximum8.78
Range5.219
Interquartile range (IQR)0.738

Descriptive statistics

Standard deviation0.70261714
Coefficient of variation (CV)0.11179921
Kurtosis1.8915004
Mean6.2846344
Median Absolute Deviation (MAD)0.3455
Skewness0.40361213
Sum3180.025
Variance0.49367085
MonotonicityNot monotonic
2023-03-24T15:51:09.851303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.713 3
 
0.6%
6.167 3
 
0.6%
6.127 3
 
0.6%
6.229 3
 
0.6%
6.405 3
 
0.6%
6.417 3
 
0.6%
6.782 2
 
0.4%
6.951 2
 
0.4%
6.63 2
 
0.4%
6.312 2
 
0.4%
Other values (436) 480
94.9%
ValueCountFrequency (%)
3.561 1
0.2%
3.863 1
0.2%
4.138 2
0.4%
4.368 1
0.2%
4.519 1
0.2%
4.628 1
0.2%
4.652 1
0.2%
4.88 1
0.2%
4.903 1
0.2%
4.906 1
0.2%
ValueCountFrequency (%)
8.78 1
0.2%
8.725 1
0.2%
8.704 1
0.2%
8.398 1
0.2%
8.375 1
0.2%
8.337 1
0.2%
8.297 1
0.2%
8.266 1
0.2%
8.259 1
0.2%
8.247 1
0.2%

AGE
Real number (ℝ)

Distinct356
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.574901
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:10.011992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.725
Q145.025
median77.5
Q394.075
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)49.05

Descriptive statistics

Standard deviation28.148861
Coefficient of variation (CV)0.41048344
Kurtosis-0.96771559
Mean68.574901
Median Absolute Deviation (MAD)19.55
Skewness-0.59896264
Sum34698.9
Variance792.3584
MonotonicityNot monotonic
2023-03-24T15:51:10.175840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 43
 
8.5%
95.4 4
 
0.8%
96 4
 
0.8%
98.2 4
 
0.8%
97.9 4
 
0.8%
98.8 4
 
0.8%
87.9 4
 
0.8%
95.6 3
 
0.6%
97 3
 
0.6%
21.4 3
 
0.6%
Other values (346) 430
85.0%
ValueCountFrequency (%)
2.9 1
0.2%
6 1
0.2%
6.2 1
0.2%
6.5 1
0.2%
6.6 2
0.4%
6.8 1
0.2%
7.8 2
0.4%
8.4 1
0.2%
8.9 1
0.2%
9.8 1
0.2%
ValueCountFrequency (%)
100 43
8.5%
99.3 1
 
0.2%
99.1 1
 
0.2%
98.9 3
 
0.6%
98.8 4
 
0.8%
98.7 1
 
0.2%
98.5 1
 
0.2%
98.4 2
 
0.4%
98.3 2
 
0.4%
98.2 4
 
0.8%

DIS
Real number (ℝ)

Distinct412
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7950427
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:10.358509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.461975
Q12.100175
median3.20745
Q35.188425
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.08825

Descriptive statistics

Standard deviation2.1057101
Coefficient of variation (CV)0.55485809
Kurtosis0.48794112
Mean3.7950427
Median Absolute Deviation (MAD)1.29115
Skewness1.0117806
Sum1920.2916
Variance4.4340151
MonotonicityNot monotonic
2023-03-24T15:51:10.511114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.4952 5
 
1.0%
5.7209 4
 
0.8%
5.2873 4
 
0.8%
6.8147 4
 
0.8%
5.4007 4
 
0.8%
6.3361 3
 
0.6%
3.9454 3
 
0.6%
6.498 3
 
0.6%
4.7211 3
 
0.6%
4.8122 3
 
0.6%
Other values (402) 470
92.9%
ValueCountFrequency (%)
1.1296 1
0.2%
1.137 1
0.2%
1.1691 1
0.2%
1.1742 1
0.2%
1.1781 1
0.2%
1.2024 1
0.2%
1.2852 1
0.2%
1.3163 1
0.2%
1.3216 1
0.2%
1.3325 1
0.2%
ValueCountFrequency (%)
12.1265 1
0.2%
10.7103 2
0.4%
10.5857 2
0.4%
9.2229 1
0.2%
9.2203 2
0.4%
9.1876 1
0.2%
9.0892 1
0.2%
8.9067 2
0.4%
8.7921 2
0.4%
8.6966 1
0.2%

RAD
Real number (ℝ)

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5494071
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:10.639793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.7072594
Coefficient of variation (CV)0.91181152
Kurtosis-0.86723199
Mean9.5494071
Median Absolute Deviation (MAD)2
Skewness1.0048146
Sum4832
Variance75.816366
MonotonicityNot monotonic
2023-03-24T15:51:10.753453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24 132
26.1%
5 115
22.7%
4 110
21.7%
3 38
 
7.5%
6 26
 
5.1%
2 24
 
4.7%
8 24
 
4.7%
1 20
 
4.0%
7 17
 
3.4%
ValueCountFrequency (%)
1 20
 
4.0%
2 24
 
4.7%
3 38
 
7.5%
4 110
21.7%
5 115
22.7%
6 26
 
5.1%
7 17
 
3.4%
8 24
 
4.7%
24 132
26.1%
ValueCountFrequency (%)
24 132
26.1%
8 24
 
4.7%
7 17
 
3.4%
6 26
 
5.1%
5 115
22.7%
4 110
21.7%
3 38
 
7.5%
2 24
 
4.7%
1 20
 
4.0%

TAX
Real number (ℝ)

Distinct66
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.23715
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:10.886119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.53712
Coefficient of variation (CV)0.4128412
Kurtosis-1.142408
Mean408.23715
Median Absolute Deviation (MAD)73
Skewness0.66995594
Sum206568
Variance28404.759
MonotonicityNot monotonic
2023-03-24T15:51:11.046700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666 132
26.1%
307 40
 
7.9%
403 30
 
5.9%
437 15
 
3.0%
304 14
 
2.8%
264 12
 
2.4%
398 12
 
2.4%
384 11
 
2.2%
277 11
 
2.2%
224 10
 
2.0%
Other values (56) 219
43.3%
ValueCountFrequency (%)
187 1
 
0.2%
188 7
1.4%
193 8
1.6%
198 1
 
0.2%
216 5
1.0%
222 7
1.4%
223 5
1.0%
224 10
2.0%
226 1
 
0.2%
233 9
1.8%
ValueCountFrequency (%)
711 5
 
1.0%
666 132
26.1%
469 1
 
0.2%
437 15
 
3.0%
432 9
 
1.8%
430 3
 
0.6%
422 1
 
0.2%
411 2
 
0.4%
403 30
 
5.9%
402 2
 
0.4%

PTRATIO
Real number (ℝ)

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.455534
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:11.207840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.05
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1649455
Coefficient of variation (CV)0.11730604
Kurtosis-0.28509138
Mean18.455534
Median Absolute Deviation (MAD)1.15
Skewness-0.80232493
Sum9338.5
Variance4.6869891
MonotonicityNot monotonic
2023-03-24T15:51:11.360972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.2 140
27.7%
14.7 34
 
6.7%
21 27
 
5.3%
17.8 23
 
4.5%
19.2 19
 
3.8%
17.4 18
 
3.6%
18.6 17
 
3.4%
19.1 17
 
3.4%
18.4 16
 
3.2%
16.6 16
 
3.2%
Other values (36) 179
35.4%
ValueCountFrequency (%)
12.6 3
 
0.6%
13 12
 
2.4%
13.6 1
 
0.2%
14.4 1
 
0.2%
14.7 34
6.7%
14.8 3
 
0.6%
14.9 4
 
0.8%
15.1 1
 
0.2%
15.2 13
 
2.6%
15.3 3
 
0.6%
ValueCountFrequency (%)
22 2
 
0.4%
21.2 15
 
3.0%
21.1 1
 
0.2%
21 27
 
5.3%
20.9 11
 
2.2%
20.2 140
27.7%
20.1 5
 
1.0%
19.7 8
 
1.6%
19.6 8
 
1.6%
19.2 19
 
3.8%

B
Real number (ℝ)

Distinct357
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.67403
Minimum0.32
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:11.534582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile84.59
Q1375.3775
median391.44
Q3396.225
95-th percentile396.9
Maximum396.9
Range396.58
Interquartile range (IQR)20.8475

Descriptive statistics

Standard deviation91.294864
Coefficient of variation (CV)0.25596162
Kurtosis7.2268175
Mean356.67403
Median Absolute Deviation (MAD)5.46
Skewness-2.8903737
Sum180477.06
Variance8334.7523
MonotonicityNot monotonic
2023-03-24T15:51:11.714046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9 121
 
23.9%
393.74 3
 
0.6%
395.24 3
 
0.6%
376.14 2
 
0.4%
394.72 2
 
0.4%
395.63 2
 
0.4%
392.8 2
 
0.4%
395.56 2
 
0.4%
390.94 2
 
0.4%
393.68 2
 
0.4%
Other values (347) 365
72.1%
ValueCountFrequency (%)
0.32 1
0.2%
2.52 1
0.2%
2.6 1
0.2%
3.5 1
0.2%
3.65 1
0.2%
6.68 1
0.2%
7.68 1
0.2%
9.32 1
0.2%
10.48 1
0.2%
16.45 1
0.2%
ValueCountFrequency (%)
396.9 121
23.9%
396.42 1
 
0.2%
396.33 1
 
0.2%
396.3 1
 
0.2%
396.28 1
 
0.2%
396.24 1
 
0.2%
396.23 1
 
0.2%
396.21 2
 
0.4%
396.14 1
 
0.2%
396.06 2
 
0.4%

LSTAT
Real number (ℝ)

Distinct455
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.653063
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:12.113268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.7075
Q16.95
median11.36
Q316.955
95-th percentile26.8075
Maximum37.97
Range36.24
Interquartile range (IQR)10.005

Descriptive statistics

Standard deviation7.1410615
Coefficient of variation (CV)0.56437413
Kurtosis0.49323952
Mean12.653063
Median Absolute Deviation (MAD)4.795
Skewness0.90646009
Sum6402.45
Variance50.99476
MonotonicityNot monotonic
2023-03-24T15:51:12.271871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.79 3
 
0.6%
14.1 3
 
0.6%
6.36 3
 
0.6%
18.13 3
 
0.6%
8.05 3
 
0.6%
5.29 2
 
0.4%
13.44 2
 
0.4%
7.44 2
 
0.4%
18.06 2
 
0.4%
5.49 2
 
0.4%
Other values (445) 481
95.1%
ValueCountFrequency (%)
1.73 1
0.2%
1.92 1
0.2%
1.98 1
0.2%
2.47 1
0.2%
2.87 1
0.2%
2.88 1
0.2%
2.94 1
0.2%
2.96 1
0.2%
2.97 1
0.2%
2.98 1
0.2%
ValueCountFrequency (%)
37.97 1
0.2%
36.98 1
0.2%
34.77 1
0.2%
34.41 1
0.2%
34.37 1
0.2%
34.02 1
0.2%
31.99 1
0.2%
30.81 2
0.4%
30.63 1
0.2%
30.62 1
0.2%

MEDV
Real number (ℝ)

Distinct229
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.532806
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-03-24T15:51:12.461371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117.025
median21.2
Q325
95-th percentile43.4
Maximum50
Range45
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation9.1971041
Coefficient of variation (CV)0.40816505
Kurtosis1.4951969
Mean22.532806
Median Absolute Deviation (MAD)4
Skewness1.1080984
Sum11401.6
Variance84.586724
MonotonicityNot monotonic
2023-03-24T15:51:12.616953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 16
 
3.2%
25 8
 
1.6%
22 7
 
1.4%
21.7 7
 
1.4%
23.1 7
 
1.4%
19.4 6
 
1.2%
20.6 6
 
1.2%
13.8 5
 
1.0%
21.4 5
 
1.0%
20.1 5
 
1.0%
Other values (219) 434
85.8%
ValueCountFrequency (%)
5 2
0.4%
5.6 1
 
0.2%
6.3 1
 
0.2%
7 2
0.4%
7.2 3
0.6%
7.4 1
 
0.2%
7.5 1
 
0.2%
8.1 1
 
0.2%
8.3 2
0.4%
8.4 2
0.4%
ValueCountFrequency (%)
50 16
3.2%
48.8 1
 
0.2%
48.5 1
 
0.2%
48.3 1
 
0.2%
46.7 1
 
0.2%
46 1
 
0.2%
45.4 1
 
0.2%
44.8 1
 
0.2%
44 1
 
0.2%
43.8 1
 
0.2%

Interactions

2023-03-24T15:51:05.993993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:45.712038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:47.339811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:49.028923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:50.617454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:52.192351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:54.004260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:55.666450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:57.319284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:59.071177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:00.681953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:02.261660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:04.057655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:06.106691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:45.827704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:47.450552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:49.141621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:50.729735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:52.438696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:54.118175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:55.788135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:57.429987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:59.190856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:00.800890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:02.396301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:04.181324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:06.230361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:45.946438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:47.570573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:49.263295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:50.847998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:52.562364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:54.240110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:55.909338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:57.551662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:59.314535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:00.929056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:02.539824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:04.326935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:06.357022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:46.077115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:47.701223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:49.385022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:50.971668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:52.685037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:54.365282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:56.045977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:57.676328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:59.435203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:01.047797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:02.687457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:04.644094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:06.493657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:46.202751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:47.821511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:49.504186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:51.084382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:52.801927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:54.484471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:56.174634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:57.939625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:59.553886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:01.162996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:02.832070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:04.782716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:06.609918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:46.331017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:47.946177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:49.629086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:51.205158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:52.928588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:54.602156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:56.307273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:58.057310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:59.676558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:01.279684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:02.969702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:04.913367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:06.728602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:46.459167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:48.163237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:49.751248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:51.324351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:53.063255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:54.719841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:56.453885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:58.178984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:59.794243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:01.400363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:03.106336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:05.039031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:06.849317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:46.592809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:48.280923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:49.870438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:51.443353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:53.200878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:54.849529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:56.574560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:58.298664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:59.915446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:01.515055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:03.238981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:05.167687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:06.975692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:46.728451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:48.408580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:49.993110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:51.568021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:53.330514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:54.982145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:56.697231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:58.418345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:00.044839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:01.636729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:03.376613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:05.298337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:07.098336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:46.849126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:48.532269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:50.112791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:51.692690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:53.462167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:55.105808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:56.815919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:58.544124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:00.166022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:01.758404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:03.515243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:05.442989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:07.214026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:46.965812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:48.649936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:50.231472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:51.812387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:53.590844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:55.241456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:56.939623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:58.660812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:00.283720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:01.874114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:03.648468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:05.575113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:07.347668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:47.099454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:48.782580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:50.369105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:51.944015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:53.725991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:55.385649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:57.072619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:58.815479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:00.422372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:02.010764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:03.791663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:05.729700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:07.476325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:47.226117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:48.913236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:50.502747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:52.071673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:53.874567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:55.530810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:57.201304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:50:58.954490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:00.561028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:02.144975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:03.934282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T15:51:05.870323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-24T15:51:12.758574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
CRIMZNINDUSNOXRMAGEDISRADTAXPTRATIOBLSTATMEDVCHAS
CRIM1.000-0.5720.7360.821-0.3090.704-0.7450.7280.7290.465-0.3610.635-0.5590.000
ZN-0.5721.000-0.643-0.6350.361-0.5440.615-0.279-0.371-0.4480.163-0.4900.4380.024
INDUS0.736-0.6431.0000.791-0.4150.679-0.7570.4560.6640.434-0.2860.639-0.5780.146
NOX0.821-0.6350.7911.000-0.3100.795-0.8800.5860.6500.391-0.2970.637-0.5630.175
RM-0.3090.361-0.415-0.3101.000-0.2780.263-0.107-0.272-0.3130.054-0.6410.6340.012
AGE0.704-0.5440.6790.795-0.2781.000-0.8020.4180.5260.355-0.2280.657-0.5480.000
DIS-0.7450.615-0.757-0.8800.263-0.8021.000-0.496-0.574-0.3220.250-0.5640.4460.084
RAD0.728-0.2790.4560.586-0.1070.418-0.4961.0000.7050.318-0.2830.394-0.3470.131
TAX0.729-0.3710.6640.650-0.2720.526-0.5740.7051.0000.453-0.3300.534-0.5620.043
PTRATIO0.465-0.4480.4340.391-0.3130.355-0.3220.3180.4531.000-0.0720.467-0.5560.157
B-0.3610.163-0.286-0.2970.054-0.2280.250-0.283-0.330-0.0721.000-0.2110.1860.052
LSTAT0.635-0.4900.6390.637-0.6410.657-0.5640.3940.5340.467-0.2111.000-0.8530.000
MEDV-0.5590.438-0.578-0.5630.634-0.5480.446-0.347-0.562-0.5560.186-0.8531.0000.210
CHAS0.0000.0240.1460.1750.0120.0000.0840.1310.0430.1570.0520.0000.2101.000

Missing values

2023-03-24T15:51:07.663822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-24T15:51:07.939112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
00.0063218.02.310.00.5386.57565.24.09001.0296.015.3396.904.9824.0
10.027310.07.070.00.4696.42178.94.96712.0242.017.8396.909.1421.6
20.027290.07.070.00.4697.18561.14.96712.0242.017.8392.834.0334.7
30.032370.02.180.00.4586.99845.86.06223.0222.018.7394.632.9433.4
40.069050.02.180.00.4587.14754.26.06223.0222.018.7396.905.3336.2
50.029850.02.180.00.4586.43058.76.06223.0222.018.7394.125.2128.7
60.0882912.57.870.00.5246.01266.65.56055.0311.015.2395.6012.4322.9
70.1445512.57.870.00.5246.17296.15.95055.0311.015.2396.9019.1527.1
80.2112412.57.870.00.5245.631100.06.08215.0311.015.2386.6329.9316.5
90.1700412.57.870.00.5246.00485.96.59215.0311.015.2386.7117.1018.9
CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
4960.289600.09.690.00.5855.39072.92.79866.0391.019.2396.9021.1419.7
4970.268380.09.690.00.5855.79470.62.89276.0391.019.2396.9014.1018.3
4980.239120.09.690.00.5856.01965.32.40916.0391.019.2396.9012.9221.2
4990.177830.09.690.00.5855.56973.52.39996.0391.019.2395.7715.1017.5
5000.224380.09.690.00.5856.02779.72.49826.0391.019.2396.9014.3316.8
5010.062630.011.930.00.5736.59369.12.47861.0273.021.0391.999.6722.4
5020.045270.011.930.00.5736.12076.72.28751.0273.021.0396.909.0820.6
5030.060760.011.930.00.5736.97691.02.16751.0273.021.0396.905.6423.9
5040.109590.011.930.00.5736.79489.32.38891.0273.021.0393.456.4822.0
5050.047410.011.930.00.5736.03080.82.50501.0273.021.0396.907.8811.9